Overview

Dataset statistics

Number of variables10
Number of observations6840
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory561.1 KiB
Average record size in memory84.0 B

Variable types

Numeric10

Alerts

Schizophrenia is highly overall correlated with Eating_disorder and 2 other fieldsHigh correlation
Bipolar_disorder is highly overall correlated with Eating_disorder and 2 other fieldsHigh correlation
Eating_disorder is highly overall correlated with Schizophrenia and 4 other fieldsHigh correlation
Anxiety is highly overall correlated with Bipolar_disorder and 2 other fieldsHigh correlation
drug_usage is highly overall correlated with Schizophrenia and 3 other fieldsHigh correlation
alcohol is highly overall correlated with drug_usageHigh correlation
mental_fitness is highly overall correlated with Schizophrenia and 4 other fieldsHigh correlation
Country is uniformly distributedUniform
Schizophrenia has unique valuesUnique
Bipolar_disorder has unique valuesUnique
Eating_disorder has unique valuesUnique
Anxiety has unique valuesUnique
drug_usage has unique valuesUnique
depression has unique valuesUnique
alcohol has unique valuesUnique
mental_fitness has unique valuesUnique

Reproduction

Analysis started2023-07-16 14:56:56.613692
Analysis finished2023-07-16 14:57:17.410563
Duration20.8 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Country
Real number (ℝ)

Distinct228
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.5
Minimum0
Maximum227
Zeros30
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size80.2 KiB
2023-07-16T20:27:17.541042image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q156.75
median113.5
Q3170.25
95-th percentile216
Maximum227
Range227
Interquartile range (IQR)113.5

Descriptive statistics

Standard deviation65.822109
Coefficient of variation (CV)0.57993048
Kurtosis-1.2000462
Mean113.5
Median Absolute Deviation (MAD)57
Skewness0
Sum776340
Variance4332.5501
MonotonicityIncreasing
2023-07-16T20:27:17.760729image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
0.4%
143 30
 
0.4%
145 30
 
0.4%
146 30
 
0.4%
147 30
 
0.4%
148 30
 
0.4%
149 30
 
0.4%
150 30
 
0.4%
151 30
 
0.4%
152 30
 
0.4%
Other values (218) 6540
95.6%
ValueCountFrequency (%)
0 30
0.4%
1 30
0.4%
2 30
0.4%
3 30
0.4%
4 30
0.4%
5 30
0.4%
6 30
0.4%
7 30
0.4%
8 30
0.4%
9 30
0.4%
ValueCountFrequency (%)
227 30
0.4%
226 30
0.4%
225 30
0.4%
224 30
0.4%
223 30
0.4%
222 30
0.4%
221 30
0.4%
220 30
0.4%
219 30
0.4%
218 30
0.4%

Year
Real number (ℝ)

Distinct30
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.5
Minimum1990
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-16T20:27:17.925229image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile1991
Q11997
median2004.5
Q32012
95-th percentile2018
Maximum2019
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.6560742
Coefficient of variation (CV)0.0043183209
Kurtosis-1.2026715
Mean2004.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum13710780
Variance74.927621
MonotonicityNot monotonic
2023-07-16T20:27:18.098279image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1990 228
 
3.3%
1991 228
 
3.3%
2018 228
 
3.3%
2017 228
 
3.3%
2016 228
 
3.3%
2015 228
 
3.3%
2014 228
 
3.3%
2013 228
 
3.3%
2012 228
 
3.3%
2011 228
 
3.3%
Other values (20) 4560
66.7%
ValueCountFrequency (%)
1990 228
3.3%
1991 228
3.3%
1992 228
3.3%
1993 228
3.3%
1994 228
3.3%
1995 228
3.3%
1996 228
3.3%
1997 228
3.3%
1998 228
3.3%
1999 228
3.3%
ValueCountFrequency (%)
2019 228
3.3%
2018 228
3.3%
2017 228
3.3%
2016 228
3.3%
2015 228
3.3%
2014 228
3.3%
2013 228
3.3%
2012 228
3.3%
2011 228
3.3%
2010 228
3.3%

Schizophrenia
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28116734
Minimum0.19162102
Maximum0.50601816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-16T20:27:18.301314image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.19162102
5-th percentile0.2080532
Q10.25546818
median0.28745649
Q30.30475992
95-th percentile0.34495671
Maximum0.50601816
Range0.31439714
Interquartile range (IQR)0.049291741

Descriptive statistics

Standard deviation0.047560805
Coefficient of variation (CV)0.1691548
Kurtosis2.4407976
Mean0.28116734
Median Absolute Deviation (MAD)0.021966537
Skewness0.70985116
Sum1923.1846
Variance0.0022620302
MonotonicityNot monotonic
2023-07-16T20:27:18.485021image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2289785884 1
 
< 0.1%
0.2744384801 1
 
< 0.1%
0.275360986 1
 
< 0.1%
0.2755383376 1
 
< 0.1%
0.2754504234 1
 
< 0.1%
0.2752265484 1
 
< 0.1%
0.2748694249 1
 
< 0.1%
0.2743845637 1
 
< 0.1%
0.273772828 1
 
< 0.1%
0.3060837638 1
 
< 0.1%
Other values (6830) 6830
99.9%
ValueCountFrequency (%)
0.19162102 1
< 0.1%
0.1916236977 1
< 0.1%
0.1916264064 1
< 0.1%
0.1916459779 1
< 0.1%
0.1916715819 1
< 0.1%
0.1917012545 1
< 0.1%
0.1917174653 1
< 0.1%
0.191773165 1
< 0.1%
0.1918503274 1
< 0.1%
0.1918926993 1
< 0.1%
ValueCountFrequency (%)
0.5060181592 1
< 0.1%
0.5059872444 1
< 0.1%
0.5056835487 1
< 0.1%
0.5055675421 1
< 0.1%
0.5050184334 1
< 0.1%
0.5040387911 1
< 0.1%
0.5038221545 1
< 0.1%
0.5013042043 1
< 0.1%
0.5005092513 1
< 0.1%
0.4966643125 1
< 0.1%

Bipolar_disorder
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.67389078
Minimum0.1893437
Maximum1.6762043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-16T20:27:18.672708image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.1893437
5-th percentile0.27841908
Q10.53979085
median0.59189321
Q30.89724786
95-th percentile1.0443839
Maximum1.6762043
Range1.4868606
Interquartile range (IQR)0.35745701

Descriptive statistics

Standard deviation0.25859406
Coefficient of variation (CV)0.3837329
Kurtosis-0.28122084
Mean0.67389078
Median Absolute Deviation (MAD)0.21879051
Skewness0.29676474
Sum4609.4129
Variance0.066870889
MonotonicityNot monotonic
2023-07-16T20:27:18.828954image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7212068563 1
 
< 0.1%
0.2673836144 1
 
< 0.1%
0.2670899267 1
 
< 0.1%
0.2670061625 1
 
< 0.1%
0.2669634574 1
 
< 0.1%
0.2669263969 1
 
< 0.1%
0.2669040823 1
 
< 0.1%
0.2668934278 1
 
< 0.1%
0.2668931877 1
 
< 0.1%
0.9025284842 1
 
< 0.1%
Other values (6830) 6830
99.9%
ValueCountFrequency (%)
0.1893437035 1
< 0.1%
0.1894145271 1
< 0.1%
0.1894228645 1
< 0.1%
0.1894936112 1
< 0.1%
0.1895492824 1
< 0.1%
0.1895880827 1
< 0.1%
0.1896059873 1
< 0.1%
0.1896390248 1
< 0.1%
0.1897001568 1
< 0.1%
0.1897121827 1
< 0.1%
ValueCountFrequency (%)
1.676204333 1
< 0.1%
1.6758283 1
< 0.1%
1.675613557 1
< 0.1%
1.674588739 1
< 0.1%
1.673943421 1
< 0.1%
1.67355677 1
< 0.1%
1.673051748 1
< 0.1%
1.672880833 1
< 0.1%
1.672525865 1
< 0.1%
1.67076418 1
< 0.1%

Eating_disorder
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21106163
Minimum0.045425136
Maximum1.1365408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-16T20:27:19.034497image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.045425136
5-th percentile0.07469938
Q10.099856656
median0.15414285
Q30.27689055
95-th percentile0.50817254
Maximum1.1365408
Range1.0911157
Interquartile range (IQR)0.1770339

Descriptive statistics

Standard deviation0.15255899
Coefficient of variation (CV)0.72281728
Kurtosis3.7550546
Mean0.21106163
Median Absolute Deviation (MAD)0.062638289
Skewness1.7123071
Sum1443.6615
Variance0.023274246
MonotonicityNot monotonic
2023-07-16T20:27:19.175197image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1310014514 1
 
< 0.1%
0.0767556178 1
 
< 0.1%
0.07689236951 1
 
< 0.1%
0.07668177689 1
 
< 0.1%
0.0764529371 1
 
< 0.1%
0.07631848603 1
 
< 0.1%
0.07572833498 1
 
< 0.1%
0.07522265389 1
 
< 0.1%
0.07445795746 1
 
< 0.1%
0.300139527 1
 
< 0.1%
Other values (6830) 6830
99.9%
ValueCountFrequency (%)
0.04542513644 1
< 0.1%
0.0454986806 1
< 0.1%
0.04555535032 1
< 0.1%
0.0455625314 1
< 0.1%
0.04560640441 1
< 0.1%
0.04561521803 1
< 0.1%
0.04567603657 1
< 0.1%
0.04574112528 1
< 0.1%
0.04579642841 1
< 0.1%
0.04582163195 1
< 0.1%
ValueCountFrequency (%)
1.13654079 1
< 0.1%
1.120082101 1
< 0.1%
1.111760481 1
< 0.1%
1.109898149 1
< 0.1%
1.10839309 1
< 0.1%
1.105394906 1
< 0.1%
1.101518904 1
< 0.1%
1.098768074 1
< 0.1%
1.094535522 1
< 0.1%
1.089447518 1
< 0.1%

Anxiety
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3275248
Minimum1.9748234
Maximum9.015948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-16T20:27:19.347284image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.9748234
5-th percentile2.7856548
Q13.5670642
median4.0944433
Q34.7972856
95-th percentile6.842225
Maximum9.015948
Range7.0411247
Interquartile range (IQR)1.2302214

Descriptive statistics

Standard deviation1.1779612
Coefficient of variation (CV)0.27220206
Kurtosis1.5055103
Mean4.3275248
Median Absolute Deviation (MAD)0.58804192
Skewness1.0823863
Sum29600.27
Variance1.3875925
MonotonicityNot monotonic
2023-07-16T20:27:19.490725image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.83512726 1
 
< 0.1%
3.988553289 1
 
< 0.1%
3.97959901 1
 
< 0.1%
3.97620389 1
 
< 0.1%
3.973003849 1
 
< 0.1%
3.968807428 1
 
< 0.1%
3.96403039 1
 
< 0.1%
3.958688577 1
 
< 0.1%
3.953423718 1
 
< 0.1%
3.884646208 1
 
< 0.1%
Other values (6830) 6830
99.9%
ValueCountFrequency (%)
1.97482337 1
< 0.1%
1.975118962 1
< 0.1%
1.976211275 1
< 0.1%
1.97771316 1
< 0.1%
1.979591946 1
< 0.1%
1.981502419 1
< 0.1%
1.983840582 1
< 0.1%
1.98735 1
< 0.1%
1.992133165 1
< 0.1%
1.998584389 1
< 0.1%
ValueCountFrequency (%)
9.015948033 1
< 0.1%
9.011537882 1
< 0.1%
8.993180683 1
< 0.1%
8.957022058 1
< 0.1%
8.921286742 1
< 0.1%
8.912767871 1
< 0.1%
8.899568877 1
< 0.1%
8.794517454 1
< 0.1%
8.791172581 1
< 0.1%
8.780291986 1
< 0.1%

drug_usage
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74670789
Minimum0.2254705
Maximum3.6995038
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-16T20:27:19.662088image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.2254705
5-th percentile0.30321993
Q10.42350185
median0.64604956
Q30.89001272
95-th percentile1.6956725
Maximum3.6995038
Range3.4740333
Interquartile range (IQR)0.46651087

Descriptive statistics

Standard deviation0.46302612
Coefficient of variation (CV)0.62009003
Kurtosis5.9398511
Mean0.74670789
Median Absolute Deviation (MAD)0.22852974
Skewness2.0788498
Sum5107.482
Variance0.21439319
MonotonicityNot monotonic
2023-07-16T20:27:19.802828image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4542022842 1
 
< 0.1%
0.6619283438 1
 
< 0.1%
0.6580957521 1
 
< 0.1%
0.6570887805 1
 
< 0.1%
0.6569776309 1
 
< 0.1%
0.6568046935 1
 
< 0.1%
0.656532118 1
 
< 0.1%
0.6572230565 1
 
< 0.1%
0.657338476 1
 
< 0.1%
0.5743411541 1
 
< 0.1%
Other values (6830) 6830
99.9%
ValueCountFrequency (%)
0.2254705009 1
< 0.1%
0.2258154474 1
< 0.1%
0.2259018952 1
< 0.1%
0.2271662293 1
< 0.1%
0.2272928831 1
< 0.1%
0.2278439453 1
< 0.1%
0.2289175821 1
< 0.1%
0.2306461492 1
< 0.1%
0.2332290604 1
< 0.1%
0.2352661944 1
< 0.1%
ValueCountFrequency (%)
3.699503796 1
< 0.1%
3.674890451 1
< 0.1%
3.63768148 1
< 0.1%
3.601911828 1
< 0.1%
3.583965529 1
< 0.1%
3.556867028 1
< 0.1%
3.552769028 1
< 0.1%
3.477397135 1
< 0.1%
3.456076121 1
< 0.1%
3.382384178 1
< 0.1%

depression
Real number (ℝ)

Distinct6840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9504487
Minimum1.6409017
Maximum7.6882128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-16T20:27:20.055383image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.6409017
5-th percentile2.7122978
Q13.2589774
median3.9041169
Q34.5505046
95-th percentile5.5205914
Maximum7.6882128
Range6.047311
Interquartile range (IQR)1.2915272

Descriptive statistics

Standard deviation0.92102112
Coefficient of variation (CV)0.23314342
Kurtosis0.59385124
Mean3.9504487
Median Absolute Deviation (MAD)0.64560918
Skewness0.55704851
Sum27021.069
Variance0.8482799
MonotonicityNot monotonic
2023-07-16T20:27:20.226972image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.125291308 1
 
< 0.1%
3.204374315 1
 
< 0.1%
3.216704974 1
 
< 0.1%
3.219831813 1
 
< 0.1%
3.220500556 1
 
< 0.1%
3.221733433 1
 
< 0.1%
3.223204271 1
 
< 0.1%
3.22318998 1
 
< 0.1%
3.221909043 1
 
< 0.1%
3.120051421 1
 
< 0.1%
Other values (6830) 6830
99.9%
ValueCountFrequency (%)
1.640901729 1
< 0.1%
1.643316907 1
< 0.1%
1.64475768 1
< 0.1%
1.650701701 1
< 0.1%
1.654644981 1
< 0.1%
1.657092984 1
< 0.1%
1.658331375 1
< 0.1%
1.658792058 1
< 0.1%
1.659790677 1
< 0.1%
1.660149307 1
< 0.1%
ValueCountFrequency (%)
7.68821275 1
< 0.1%
7.683887867 1
< 0.1%
7.668643628 1
< 0.1%
7.661370891 1
< 0.1%
7.648538117 1
< 0.1%
7.622935952 1
< 0.1%
7.597049319 1
< 0.1%
7.596065418 1
< 0.1%
7.541464815 1
< 0.1%
7.510545327 1
< 0.1%

alcohol
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5788072
Minimum0.31990044
Maximum4.698694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-16T20:27:20.727535image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.31990044
5-th percentile0.43586009
Q10.73282581
median1.4600449
Q32.2612621
95-th percentile3.2434462
Maximum4.698694
Range4.3787936
Interquartile range (IQR)1.5284362

Descriptive statistics

Standard deviation0.93465468
Coefficient of variation (CV)0.59200053
Kurtosis-0.11392384
Mean1.5788072
Median Absolute Deviation (MAD)0.76201831
Skewness0.67075305
Sum10799.041
Variance0.87357937
MonotonicityNot monotonic
2023-07-16T20:27:20.915061image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4440362593 1
 
< 0.1%
0.7735020564 1
 
< 0.1%
0.7738016849 1
 
< 0.1%
0.7743088341 1
 
< 0.1%
0.7750878582 1
 
< 0.1%
0.7761362663 1
 
< 0.1%
0.7773155408 1
 
< 0.1%
0.7786910939 1
 
< 0.1%
0.7800215278 1
 
< 0.1%
1.65435756 1
 
< 0.1%
Other values (6830) 6830
99.9%
ValueCountFrequency (%)
0.3199004366 1
< 0.1%
0.3200389816 1
< 0.1%
0.3205738015 1
< 0.1%
0.3213131856 1
< 0.1%
0.3216675876 1
< 0.1%
0.3236255197 1
< 0.1%
0.3242706774 1
< 0.1%
0.328263524 1
< 0.1%
0.3328015483 1
< 0.1%
0.3373837907 1
< 0.1%
ValueCountFrequency (%)
4.698694015 1
< 0.1%
4.616114141 1
< 0.1%
4.612252118 1
< 0.1%
4.606228044 1
< 0.1%
4.599298252 1
< 0.1%
4.595736547 1
< 0.1%
4.590083316 1
< 0.1%
4.584368745 1
< 0.1%
4.56985219 1
< 0.1%
4.565493558 1
< 0.1%

mental_fitness
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6840
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8180618
Minimum0.21564704
Maximum13.761517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size106.9 KiB
2023-07-16T20:27:21.104900image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0.21564704
5-th percentile1.4242863
Q13.0065069
median4.6791771
Q36.3874877
95-th percentile8.7846474
Maximum13.761517
Range13.54587
Interquartile range (IQR)3.3809808

Descriptive statistics

Standard deviation2.2940293
Coefficient of variation (CV)0.47613115
Kurtosis-0.24642479
Mean4.8180618
Median Absolute Deviation (MAD)1.6858145
Skewness0.40347843
Sum32955.543
Variance5.2625704
MonotonicityNot monotonic
2023-07-16T20:27:21.292368image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.696670483 1
 
< 0.1%
2.367970825 1
 
< 0.1%
2.481643798 1
 
< 0.1%
2.446697652 1
 
< 0.1%
2.419505634 1
 
< 0.1%
2.391383922 1
 
< 0.1%
2.366675859 1
 
< 0.1%
2.338679761 1
 
< 0.1%
2.363714347 1
 
< 0.1%
6.795390495 1
 
< 0.1%
Other values (6830) 6830
99.9%
ValueCountFrequency (%)
0.2156470363 1
< 0.1%
0.7118362378 1
< 0.1%
0.7123795495 1
< 0.1%
0.7305127997 1
< 0.1%
0.7389982923 1
< 0.1%
0.754945144 1
< 0.1%
0.7598667133 1
< 0.1%
0.7977303732 1
< 0.1%
0.8014678267 1
< 0.1%
0.8175713145 1
< 0.1%
ValueCountFrequency (%)
13.76151657 1
< 0.1%
13.72057186 1
< 0.1%
13.69250903 1
< 0.1%
13.67020102 1
< 0.1%
13.60839063 1
< 0.1%
13.43594058 1
< 0.1%
13.36733778 1
< 0.1%
13.23790297 1
< 0.1%
13.02193767 1
< 0.1%
12.6947323 1
< 0.1%

Interactions

2023-07-16T20:27:15.009719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:26:56.953385image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:26:59.145296image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:00.971146image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:02.660363image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:04.485667image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:06.437983image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:08.105217image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:10.772142image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:12.785612image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:15.271971image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:26:57.110225image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:26:59.337953image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:01.195045image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:02.852962image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:04.700562image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:06.597422image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:08.282350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:10.932835image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:13.010660image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:15.533194image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:26:57.303952image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:26:59.556074image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:01.351725image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:03.040027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:04.896976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-16T20:27:08.459272image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:11.108040image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:13.227998image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:15.695501image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:26:57.609066image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:26:59.751699image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:01.520410image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:03.211906image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:05.137383image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:06.998313image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:08.616882image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:11.273645image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:13.484421image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:15.883002image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:26:57.926180image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:26:59.933621image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:01.670377image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:03.352487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:05.266075image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:07.173764image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:08.834846image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:11.474680image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:13.755224image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:16.053993image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:26:58.128930image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:00.111060image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:01.828886image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-16T20:27:08.991953image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:11.626420image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:13.973968image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:16.246680image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:26:58.305994image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:00.299565image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-16T20:27:03.649358image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:05.659622image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:07.481149image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:10.025655image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:11.805365image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:14.232051image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:16.449095image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:26:58.493578image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
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2023-07-16T20:27:05.861419image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:07.643354image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:10.238369image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:12.035343image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:14.409712image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:16.623403image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:26:58.756643image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:00.635711image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:02.336493image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:04.051503image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:06.071856image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:07.791538image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:10.395484image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:12.288346image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:14.591679image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:16.779864image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:26:58.957620image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:00.780454image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:02.508183image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:04.238163image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:06.269205image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:07.927870image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:10.565600image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:12.545184image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-07-16T20:27:14.733815image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-07-16T20:27:21.432993image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
CountryYearSchizophreniaBipolar_disorderEating_disorderAnxietydrug_usagedepressionalcoholmental_fitness
Country1.0000.0000.039-0.130-0.073-0.0430.031-0.081-0.048-0.022
Year0.0001.0000.0720.0230.0970.0460.053-0.036-0.0110.190
Schizophrenia0.0390.0721.0000.1430.5720.3030.696-0.4910.3530.633
Bipolar_disorder-0.1300.0230.1431.0000.7740.6300.3740.2580.3420.645
Eating_disorder-0.0730.0970.5720.7741.0000.6590.6520.0130.3800.846
Anxiety-0.0430.0460.3030.6300.6591.0000.4680.1240.1180.673
drug_usage0.0310.0530.6960.3740.6520.4681.000-0.2680.5280.573
depression-0.081-0.036-0.4910.2580.0130.124-0.2681.000-0.057-0.065
alcohol-0.048-0.0110.3530.3420.3800.1180.528-0.0571.0000.217
mental_fitness-0.0220.1900.6330.6450.8460.6730.573-0.0650.2171.000

Missing values

2023-07-16T20:27:17.012667image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-16T20:27:17.285448image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CountryYearSchizophreniaBipolar_disorderEating_disorderAnxietydrug_usagedepressionalcoholmental_fitness
0019900.2289790.7212070.1310014.8351270.4542025.1252910.4440361.696670
1019910.2281200.7199520.1263954.8217650.4471125.1163060.4442501.734281
2019920.2273280.7184180.1218324.8014340.4411905.1065580.4455011.791189
3019930.2264680.7174520.1179424.7893630.4355815.1003280.4459581.776779
4019940.2255670.7170120.1145474.7849230.4318225.0994240.4457791.712986
5019950.2247130.7166860.1111294.7808510.4285785.0984950.4454221.738272
6019960.2236900.7163880.1077864.7772720.4263935.1005800.4448371.778098
7019970.2224240.7161430.1039314.7752420.4237205.1054740.4439381.781815
8019980.2211290.7161390.1003434.7773770.4224915.1137070.4426651.729402
9019990.2200650.7163230.0979464.7820670.4212155.1204800.4414281.850988
CountryYearSchizophreniaBipolar_disorderEating_disorderAnxietydrug_usagedepressionalcoholmental_fitness
683022720100.2071800.5566400.0944903.2915690.6189483.4471271.7890211.606235
683122720110.2073360.5571040.0948713.2929640.6138753.4574061.7793801.758867
683222720120.2077410.5579080.0956393.2971240.6093783.4806001.7687821.905674
683322720130.2082800.5588880.0969503.3028810.6038913.5085341.7577762.024167
683422720140.2088570.5599290.0983563.3093900.6007243.5337371.7466752.112216
683522720150.2093590.5608820.0996103.3157010.5996043.5486131.7349692.193166
683622720160.2099790.5617680.1008213.3242300.6036583.5575081.6892812.279813
683722720170.2106310.5626120.1016713.3305690.6080963.5641381.6518052.364265
683822720180.2112370.5632830.1023983.3175000.6090653.5631411.6867112.472949
683922720190.2119690.5638200.1029023.2839340.6106443.5545711.7767292.525892